From image toinsightZEISS ZEN AI ToolkitYour Versatile Machine Learning Package
for Image Analysis
zeiss.com/zenA Companion on Your Analysis Journey
Smart Tools for Every Step along the Workflow

Machine Learning in Image Analysis
Today’s imaging systems can reveal the most intricate processes of life; however, transforming thisimage data into meaningful scientific insight can be intensive and lengthy. Image processing also has
grown more complex. With the AI Toolkit, a collection of ZEN Intellesis tools, ZEISS has integrated
modern Machine Learning capabilities that allow you to maintain your own intuitive language on
your journey from image to data. All you need for success is your life science expertise.
Acquire dataset

Re-use trained models to denoise, segment andclassify any number of similar data

Intellesis Denoising
Let Intellesis enhance yourimages

Label and train
segmentation model
based on a subset

Intellesis Segmentation
Let Intellesis segment
the entire dataset

Label and train
classification model
based on a subset

Intellesis Classification
Let Intellesis classify theentire dataset

Step 2 – Segmentation

Step 3 – Classification

Even with modern imaging systems, raw

With optimally preprocessed imag-

The work is not complete simply

images still contain image i­mperfections

es, the next step is to segment objects

because objects have been identified.

– suboptimal signal-to-noise ratio orof interest in the image. This can be

It may be necessary to further classify

artifacts like shot or camera noise –a notoriously tedious task, requiringthem based on their appearance, tex-

which can be easily corrected withexpert knowledge of the many algo-ture, shape, or other features. This pro-Intellesis Denoising. The Deep Learn-rithms available and their underlying

cess is radically simplified by Machine

ing algorithm requires no additional

data structures. Intellesis Segmentation

Learning. With Intellesis Object Classi-

training data and is very intuitive to use

employs a completely different para-

fication, you assign some characteristic

without the need for extensive parame-

digm. Instead of setting up a series ofobjects of your dataset to the desired

ter settings. Intellesis Denoising produc-complex methods, you simply annotate

classes. These assignments are thenes smoothed, contrast-enhanced imag-

some of your favorite objects of interest

learned by a Machine Learning modeles. It can be used as a stand-alone toolwith a brush tool to teach a Machine

and applied to the entire dataset. Com-or applied after other image process-Learning model. Previously impossible

pared to conventional methods, you

ing methods such as Deconvolution,

segmentation challenges become fea-

benefit from a simpler procedure with

­A potome or Airyscan processing.

sible.

high-quality results.

Step 1 – Denoising

Title image: Drosophila embryo images recorded with ZEISS Celldiscoverer 7 and Airyscan 2. Images Courtesy of University of Gothenburg

2Harnessing the Power of Deep Learning
The Fastest Bridge from Image to Information

The AI Toolkit provides easy accessTrain. Eat. Sleep. Repeat.

AI-Ready Beyond ZEN

to rapid and robust image ­denoising,Today’s science demands reproducibili-

Machine Learning is a highly d­ ynamic,segmentation and classification. Stayty and throughput. Machine Learning isdeveloping field involving an active

focused on your biology and let themade for that. Once trained for a spe-­academic community. To facilitate thesoftware take care of the technicalcific task, a model can be re-used foruse of the latest tools, ZEISS madedetails.

similar data.

Intellesis an open, adaptable machine

Intuitive for Life Scientists

Employ Your Hardware Efficiently

from other software platforms can be

The beauty of Machine Learning appli-A Machine Learning tool requires aimported and employed easily. ZEISS’scations is their ease of use. You don’t

lot of processing power. ­F ortunately,

public image analysis platform, APEER,

need expert knowledge of image anal-resources for that are standard forhas a dedicated machine learning sec-

ysis and are almost completely relieved­modern multi-core CPU computers.

tion to address the continuously grow-from method selection and parameter

The AI Toolkit is designed to run withing number of applications.

fine-tuning. Instead, you instruct thehigh degrees of parallelization andalgorithm by providing examples of seg-employs GPU processing to get you

Try it on Your Dataset

mentation or classification. The denois-

accurate results quickly.

The AI Toolkit is extremely flex-

learning platform. Pre-trained models

ible and allows segmenting any

ing algorithm does not require any
Integration in Imaging Workflows­multi-dimensional image from light,

Intellesis is well-embedded into ZEN’selectron or x-ray microscopes. No mat-More Than a Sum of Pixel Values

image analysis tools to unleash the fullter whether your focus is cell biology,

Conventional image analysis methods

power of Machine Learning segmenta-

neuroscience or developmental biolo-

almost exclusively employ basic f­ eatures

tion and classification. Use these tools

gy, or if you regularly use cell culture,

like pixel intensities. But images aren’t

in automated workflows with the Imageorganoids, tissues or whole organisms,

just pixels; they have texture and shape

Analysis Wizard or Python scripting toIntellesis will make your image analysis

and object relationships. Machine

extract data and generate publishable

easier and more reliable. Now, it’s your

Learning algorithms take all thesevisualizations. Or, employ the AI Toolkit

turn to test it on your dataset.

­attributes into account, which is what

in correlative experiments along withmakes the AI Toolkit uniquely capable

other ZEN workflow packages:of tackling more complex tasks.

• 2D Toolkit

adjustments at all.

• 3D Toolkit• Smart Acquisition Toolkit• Bio Apps Toolkit

U2OS + LLC2 cell culture – classification of cellular phenotypes based on nuclear mCherry and cytosolic GFP fluorescence (left image). Cells were segmented
with conventional threshold segmentation. Then, a few cells were manually assigned with Intellesis Classification (center image). Intellesis finally predicted thecomplete dataset (right image).

3

Denoised image

Reference (high SNR)

Convolaria, acquired with ZEISS Axio Imager and processed with AI Denoising
A short acquisition time (0.2 ms) results in a noisy raw image (left panel), but the signal-to-noise ratio (SNR) can be improved significantly by AI Denoising postprocessing (center panel). A high-SNR image (acquisition time 150 ms) is shown as reference (right panel).

ZEISS ZEN AI Toolkit features

Availability• Denoising, segmentation, and classification of every image

The AI Toolkit is available for the following systems andtype readable by ZEN• Compatible with multi-dimensional datasets including
stacks and tiled images• Intuitive training interfaces for segmentation and­s oftware:• ZEN 3.6 (blue edition)• Windows 10, 64 Bit Systems• 64 GB RAM + 8 GB GPU (recommended)classification• Import of deep-learning models from APEER and externaltools (AI-ready)Note: The module ZEN Intellesis Object Classification requires
an analyzed image created with ZEN Image Analysis.

• Seamless integration within ZEN framework and Image
Analysis Wizard• Export of segmented images to third-party software tools• Exchange and sharing of models via an open model format• Parallel and GPU computing• Large data handling• Well-established open-source Machine Learning algorithms,powered by Python, TensorFlow, ONNX, Scikit-Learn and Dask

Carl Zeiss Microscopy GmbH07745 Jena, Germany
microscopy@zeiss.com
www.zeiss.com/zen

APEER ML provides an easy way
to train your own Deep Learningmodels to be used in ZEN.
No expertise or coding required.
www.apeer.com

Not for therapeutic, treatment or medical diagnostic evidence. Not all products are available in every country. Contact your local ZEISS representative for more information.
EN_41_012_257 | CZ 01-2023 | Design, scope of delivery, and technical progress subject to change without notice. | © Carl Zeiss Microscopy GmbHRaw image (low SNR)